• Digitalisation of the agrifood sector: what does Twitter tell us?

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    Technology is advancing at a frenetic pace and offers the agrifood chain a large number of opportunities to make its production more efficient and sustainable. Moreover, the arrival of COVID-19 has shown that the most digitalised companies were able to continue their activities more readily than the rest. In this article we examine the degree of popularity of the different digital technologies used in the primary sector and agrifood industry based on a text analysis of over 2 million tweets on Twitter. All these technologies are essential to create a connected ecosystem that will make up the Food Chain 4.0 of the future.

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    The unexpected arrival of the pandemic has shown that the most digitalised companies were more prepared to adapt to the new situation and were able to continue to operate much more smoothly than the rest. There is no doubt that, in this new environment, the digital transformation of companies is now unavoidable in order to boost their competitiveness.

    Big data, robotics, the internet of things and blockchain are just some examples of the new digital technologies gradually being adapted by firms, particularly in the agrifood sector. Technology is advancing at a frenetic pace and is offering the agrifood chain a large number of opportunities to produce more efficiently and sustainably. However, statistical information on the degree to which such technologies have been taken up, and the most comprehensive official statistical source1, does not provide information on the primary sector. Below we present a novel analysis of the «popularity»  of new digital technologies in the agrifood sector based on data from Twitter.

    • 1. Survey on the use of information and communication technologies (ICT) and e-commerce in companies, compiled by the National Statistics Institute.
    Twitter as a source of information to detect future trends

    Data from Twitter can be extremely valuable in detecting new trends as it allows us to analyse the popularity of certain terms according to how frequently they appear in tweets. However, it is true that «talking about something» is not the same as successfully implementing the various digital technologies in a company's recurring operations. For this reason the results presented below should be interpreted simply as an indication of new trends that may be taking root in agrifood companies.

    Data from Twitter allow us to analyse how popular the different digital technologies

    are in the agrifood sector according to how often they are mentioned in tweets.

    For this study, data was processed from over 24 million tweets sent by individual users and digital media during the period 2017-2019. Among these, 2 million corresponded to the agrifood sector. Using natural language processing techniques, the tweets were categorised according to mentions of different digital technologies and to the business sector.2 The key to obtaining relevant data from social media is to first define «seed» words or phrases to identify texts corresponding to each of the business sectors, as well as «seed» words or phrases related to the different digital technologies of interest.3 Using a machine-learning algorithm, other words and phrases related to the concept in question that were not initially included were also identified, thus broadening the spectrum of texts analysed. At this stage, it is important to carefully screen for polysemous words (i.e. those that have more than one meaning, such as the word «reserva» in Spanish, which can be used to refer to a hotel booking as well as an aged wine).

    • 2. This analysis was carried out in collaboration with Citibeats, a company specialising in unstructured natural language processing.
    • 3. For example, the «seed» woods and phrases used to identify big data were: analytics, arquitectura de sistemas (system architecture), data mining, database, inteligencia empresarial (business intelligence), Python and SQL, among others (as well as the term big data per se).
    What is the degree of digitalisation of the agrifood sector according to Twitter?

    To assess the agrifood sector's degree of digitalisation according to data from Twitter, we first need to know how common tweets about digitalisation are in other business sectors. The most digitalised industry according to our analysis is the information and communication technologies (ICT) sector: 3.2% of the sector's tweets contain terms related to digitalisation, a result that is not surprising given the very nature of the industry. Next comes finance and insurance with 2.7% of the tweets.

    This percentage is obviously lower in the primary sector at 0.6% but it is similar to the 0.7% for professional, scientific and technical activities. In the case of the agrifood industry, the percentage of tweets on digitalisation is only 0.3%, very close to the basic manufacturing sector (which includes the textile, wood, paper and graphic arts industries), with the lowest percentage among the sectors analysed, 0.2%.

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    Which digital technologies are most popular in the agrifood sector according to Twitter?

    The wealth of data obtained from Twitter allow us to identify the most popular digital tools in each business sector according to how frequently they are mentioned in the tweets examined. According to our analysis, a large proportion of the primary sector's tweets about digitalisation tend to include issues related to big data (45% of all tweets about digitalisation). One clear example of the application of big data in the sector can be found in «precision agriculture» techniques which require large amounts of data to be analysed to optimise decisions and thereby increase production and, in turn, ensure sustainability. These techniques are used, for instance, to calculate the irrigation requirements of crops by taking into account climatic conditions (sunlight, wind, temperature and relative humidity) and crop characteristics (species, state of development, planting density, etc.). To carry out this calculation, real-time updated meteorological data, a large computing capacity and fast data transmission speeds are all required for an automatic irrigation system to be properly adjusted. This technology helps to use water more efficiently, a highly relevant aspect in areas with a Mediterranean climate that are extremely vulnerable to climate change and where water is in short supply.

    Big data, the internet of things and robotics are the most popular technologies in the primary sector,

    indispensable for advancing the application of precision agriculture techniques and smart automated farming.

    Other popular technologies in the primary sector are the internet of things (16% of tweets) and robotics, including drones (10% of tweets). The new digital technologies promise to revolutionise the field of agriculture and stockbreeding by the middle of this century, the same as the mechanisation of farming in the xxi century. Agricultural Machinery 4.0 (which is closer to the robots in science fiction films than to the tractors we are used to seeing on all farms in the country) helps to increase productivity whilst also improving working conditions in the field. This trend towards more automated agricultural tasks has become stronger in the wake of the coronavirus pandemic, as the difficulty in recruiting seasonal workers due to international mobility restrictions has led to increased interest in robotics and agricultural automation. In fact, companies that manufacture robots for agriculture have seen a sharp increase in orders, such as robots that pick strawberries while removing mould with ultraviolet light.14 

    The use of drones warrants particular attention as this has grown exponentially in recent years and applications are increasingly widespread: from the early detection of pests and the aerial inspection of large areas of crops to locating wild boar with heat-sensitive cameras to prevent the spread of African swine fever to domestic pigs.5

    • 4. See Financial Times Agritech «Farm robots given Covid-19 boost», 30 August 2020.
    • 5. See http://www.catedragrobank.udl.cat/es/actualidad/drones-contra-jabalies

    The popularity of various digital technologies in the agrifood sector

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    Blockchain is the technology that stands out most in the food sector (30% of the total number of tweets on the sector's digitalisation) and this comes as no surprise as it has many different applications for the food and beverage industry. Producing a chain of unalterable, reliable records, blockchain makes it possible to guarantee the complete traceability of products throughout all the links in the food chain. Simply scanning a QR code provides access to all the data regarding the origin, production method, veterinary treatments received, ingredients used, etc. A large number of agrifood companies are already experimenting with blockchain as it offers clear benefits in terms of transparency regarding origin, product quality and food safety, aspects that are increasingly valued by consumers. Blockchain technology is also being used to limit food waste, another essential challenge for the sector.

    Blockchain enables the digital verification of food products,

    making them traceable throughout the links in the food chain.

    Compared with other sectors, which tools are particularly significant for the agrifood industry?

    There are some digital technologies that are not very popular across all economic sectors, perhaps because they have a more limited or specific range of application. These are technologies that, despite having a low percentage of tweets in absolute terms according to our study, may be relatively popular for a particular sector compared with the rest.

    To detect such cases, we have calculated a new metric, namely a concentration index which takes into account the relative popularity of technologies in a sector compared with the rest of the sectors.6 By using this methodology, we have found that the primary sector continues to stand out in terms of big data. Specifically, the primary sector concentrates 9.2% of the total number of tweets mentioning big data made by all sectors, a much larger proportion than the 3.1% share of primary sector tweets out of the total number of tweets analysed (as can be seen in the following table, in this case the concentration index is 3). We have also determined that the sector is particularly interested in the internet of things, as already mentioned, but have discovered that nanotechnology is also a relatively popular technology in the primary sector. In other words, although only 3.8% of the tweets in the primary sector deal with nanotechnology, this percentage is high compared with the 1.7% share of nanotechnology tweets out of the total (in other words, this technology is not very popular in general across all sectors but is slightly more popular in the primary sector than the others). This find is not surprising since genetic engineering is one of the fields in which technology has advanced most in order to boost crop yields. For example, by optimising the yield of vines it is possible to develop plants that are much more resistant to extreme weather conditions and pests.

    • 6. The concentration index is calculated as the ratio between (1) the percentage of tweets related to a particular technology and sector out of the total tweets for this technology, and (2) the percentage of tweets by a sector out of the total tweets of all sectors. Values above 1 indicate the technology is relatively more popular in that sector.

    Concentration index for tweets related to each technology in comparison with the other sectors

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    Finally, virtual and augmented reality is also a relatively popular technology in
    the agrifood industry.
    Specifically, the agrifood industry concentrates 6.2% of the total virtual and augmented reality tweets made by all sectors, a percentage that more than doubles the 2.5% share of primary sector tweets out of the total number of tweets analysed (the concentration index is equal to 2.5 in this case). This technology uses virtual environments (virtual reality) or incorporates virtual elements into reality (augmented reality) that provide additional knowledge and data that can be used to optimise processes. At first it may be surprising that this technology is relatively popular in the agrifood industry but its uses are spreading as the industry implements digital technologies in its production processes, in the so-called Industry 4.0. One specific example of how this technology is used is in repairing breakdowns. When a fault occurs, operators can use augmented reality goggles to follow the steps contained in virtual instruction manuals that are projected onto the lens to help resolve the incident. The glasses recognise the different parts of the machine and visually indicate to operators where they should act to solve the specific problem.

    There are numerous examples of new digital technologies being applied in the agrifood sector. We are witnessing a revolution that is destined to transform the different links in the food chain: from the exploitation of data and the use of drones to make harvesting more efficient to implementing blockchain technology to improve the traceability of the final products that reach our homes. In short, the future will bring us the Food Chain 4.0, a totally connected ecosystem from the field to the table.

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The digital economy: the challenge of measuring a technological revolution

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The digital economy, or new economy, has come of age. The statistics which, as we shall see later, have so many caveats in capturing the extent of digitisation have at least been able to clearly detect the «core» of that process, i.e. the dissemination of information and communication technologies (ICT) since the mid-1990s. Yet there are still great difficulties in measuring the full extent of digitisation, primarily because part of it (perhaps most of it, in fact) falls outside traditional market exchanges and, consequently, is not captured in the conventional statistics. In this article, we will start by looking at the existing measures that are used to build these statistics, before offering some examples of additional measures. Considering these measures as a whole, it emerges a vision of the economy that is, perhaps, somewhat different to what we are used to: we are no doubt now living in a world with more growth, less inflation and greater well-being.

Measuring the «core»: the digital economy, in the strict sense of the term, represents less than 10% of the total economy

In most countries with modern national statistics, starting in the mid-1990s an acceleration occurred in the diffusion of ICT, which constitutes the technological core of the digital economy.

The most ambitious measurement effort to date was undertaken by a team of economists from the Bureau of Economic Analysis (BEA) in the US (Barefoot et al., 2018),1 which has developed a satellite set of the US national accounts that measures the digital economy.2 Its methodological approach, which probably anticipates what other statistical institutes will do in the future, is as follows:

They define the digital economy as that which integrates ICT infrastructure, the exchange of digital goods and services (e-commerce) and digital content.

Using information from the supply side of the economy, they use the 5,000 categories of goods and services and select 200 types of products and services that they consider to be digital.

Finally, they identify the sectors that produce these 200 goods and services and, for each of these sectors, they disentangle the part that is authentically digital from that which is conventional. Then, for each sector they estimate the value added and other economic measures of the digital segment.

As a result of this exercise, Barefoot et al. (2018) obtained three key results:

If the sum of the digital segments from all sectors that provide digital goods and services are added together and compared with the conventional sectors, they concluded that, in 2016, the digital economy represented 7% of GDP in the US, ahead of sectors such as retail. This estimate is consistent with another by the IMF (2018), in which it is noted that, in many countries, the digital sector represents less than 10% of the value added, income or total employment3

The digital economy is more dynamic than the conventional one: between 2006 and 2016, the latter grew at an average annual rate of 1.5%, while the digital economy did so at an average of 5.6%.

The digital economy is less inflationary than the traditional one: in the same period from 2006 to 2016, while the prices of conventional goods and services grew at an annual average of 1.5%, those of digital goods and services fell by 0.4% per year.

If we correct for the undervaluation of digital goods and services, GDP growth could be significantly higher than conventional estimates suggest

These figures, and in particular that of the weight of the digital sector in the economy as a whole, may seem somewhat disappointing to the readers, who perceive that the digital world pervades virtually all areas of the economy and society. The truth is that, despite these figures having the virtue of being methodologically rigorous and, therefore, the ability to be integrated into a national accounting system without any problems, they do not address the two essential problems for adequately measuring the digital economy:

Many digital transactions do not have an explicit market price.

Digital products and services are subject to rapid changes in quality and obsolescence, which makes it difficult to correctly calculate prices (for instance, is the first smartphone, which is not only worth many times more than any previously existing mobile phone but also has many times the performance, the same product as a conventional mobile phone? How should this, then, be incorporated into the basket of goods for determining the CPI?).

In order to compensate for these limitations, at least partly, attempts have been made to calculate additional measures. At this point, let us explore two alternatives that seek to monetise that part of the digital economy that has no explicit price:

The first alternative is to treat «free» goods in the same way as free public services, i.e. valuing them at their cost of production. An example of such an approach is that used by Nakamura et al. (2018)4, which estimates the production costs of free digital media and other similar services based on the income generated from advertising and marketing (the idea is that, in fact, there is a transaction in which the consumer enjoys the free digital good in exchange for consuming the advertisements and marketing material). On this basis, this approach estimates that the annual GDP growth of the US in the period 2005-2015 would have been 0.10 pp higher than the conventional estimate.

The second major alternative is to simulate a hypothetical market and use it to try to infer the value of certain digital goods. This approach, which is frequently used in environmental economics to value intangible goods such as landscape, is that followed, among others, by Brynjolfsson and co-authors (2018).5 These authors conducted different experiments with a sample of users of digital applications in order to estimate how much they would be willing to pay for the free services they enjoyed, based on the value they extracted from using such applications. In the case of Facebook, for instance, it was estimated that it had added more than 1 decimal point per year to GDP growth between 2007 and 2013.

As can be seen, these figures suggest that the undervaluation of digitisation in GDP could be significant, since even these exercises conducted for specific digital goods indicate a sizable impact.

Spain and Portugal have made progress in the dissemination of digitisation, but remain mid-way in the European ranking

Among the approaches that seek to complement conventional measures, specific efforts are being made to better approximate the penetration rate of digitisation using new indicators. This approach highlights, for example, the work that has been done in the EU through the so-called digital economy and society index (DESI), which calculates a measure of the dissemination of digitisation by taking into consideration five factors: i) connectivity (25% weight in the total index), ii) human capital (25%), iii) internet use (15%), iv) integration of digital technology (20%) and v) digital public services (15%). One of the main virtues of the DESI is that it allows us to make an approximation for Spain and Portugal, two economies where there are few statistics on digitisation.

Although the DESI covers only a relatively short period of time (2014-2018), some initial conclusions can nevertheless be drawn:

In 2018, Spain’s position in terms of digital distribution was slightly above the EU average, while that of Portugal was slightly below. In recent years, Spain has tended to climb the ranking, while Portugal has dropped down.

Based on the degree of economic development in Spain and Portugal, what ought to be their level of digital penetration? The data suggest that both Spain and Portugal have a level of digitisation that is slightly higher than would be expected for their level of income. In any case, both countries trail far behind the economies whose degree of digitisation is significantly higher than that of their relative prosperity, such as the Nordic countries and the Netherlands.

It is also important to identify relative strengths and weaknesses. Both countries stand higher in the ranking in the field of digital public services (Spain is the fourth best in the EU and Portugal, the ninth), while Spain also scores well in connectivity, an area which has seen a significant improvement over the past four years. The weakest point in the Iberian economies is human capital, although it should be recognised that Portugal and, to a lesser extent, Spain have improved since 2014.

In conclusion: we are living in a world of faster growth and lower inflation and, in this world, very few sectors will be unaffected by digital disruption

If the measures of the digital sector were more accurate, it would probably become more clear that we are, in fact, in an economy with an effective growth that may be higher than that conventionally considered, which could be operating with levels of inflation below those published and which could also be generating higher well-being among consumers than previously thought.

In this digital economy, the view we had just four years ago (Masllorens and Ruiz, 2015), in which we differentiated between «pure» sectors (that operate entirely in the digital world), «revolutionised» sectors (whose value chain has undergone a complete transformation due to digitisation) and «traditional» sectors (whose value chain has not undergone any significant disruption), may have lost much of its meaning: it is increasingly difficult to identify «traditional» sectors, and it will probably become even more so in the future.6

Àlex Ruiz

1. K. Barefoot, D. Curtis, W.A. Jolliff, J.R. Nicholson and R. Omohundro (2018). «Defining and Measuring the Digital Economy». US Department of Commerce Bureau of Economic Analysis, Washington, DC, 15.

2. The satellite set of the national accounts are used to segregate specific areas or sectors and to reflect all the relevant economic information in them.

3. IMF (2018). «Measuring the digital economy». Staff Report, Policy Papers, April.

4. L.I. Nakamura, J. Samuels and R.H. Soloveichik (2017). «Measuring the ‘Free’ Digital Economy within the GDP and Productivity Accounts», Working Paper n° 17-37, Federal Reserve Bank of Philadelphia.

5. E. Brynjolfsson, W.E. Diewert, F. Eggers, K.J. Fox and A. Gannamaneni (2018). «The Digital Economy, GDP and Consumer Welfare: Theory and Evidence». ESCoE Conference on Economic Measurement, Bank of England.

6. See the article «The digital economy: the global data revolution», in the Dossier of the MR07/2015.

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